Advanced identification and classification of sensors and other points in a building automation system

ABSTRACT

A building automation space facility or building automation facility comprising systems and methods for classifying and standardizing sensors and sensor data using time-series data and/or meta data, such as data and meta information available in a control network or hardware is disclosed. The disclosed system and methods provide techniques to rapidly identify and classify sensors using analysis of time-series data (e.g., data collected from a sensor such as by polling the sensor every second, minute, hour, day, month, and so on) and/or meta data (e.g., label names, group names, associated equipment, and so on) from these sensors and associated control systems. In this manner, the disclosed techniques greatly improve the extent to which a building automation facility or system monitors, manages, and reports on various elements within a building or group of buildings.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.15/201,222 filed on Jul. 1, 2016, titled ADVANCED IDENTIFICATION ANDCLASSIFICATION OF SENSORS AND OTHER POINTS IN A BUILDING AUTOMATIONSYSTEM, which claims priority to U.S. Provisional Patent Application No.62/188,308 filed on Jul. 2, 2015, titled ADVANCED IDENTIFICATION ANDCLASSIFICATION OF SENSORS BASED ON NAMES, META INFORMATION, AND TIMESERIES DATA, each of which is herein incorporated by reference in itsentirety.

BACKGROUND

Building automation systems enable building owners to, among otherthings, quickly and easily monitor activity within the building, measureenvironmental factors within the building, identify inefficientprocesses or equipment within the building, and so on. Buildingautomation systems are enabled by a number of sensors and other pointsthat may be distributed throughout the building, such as temperaturesensors, humidity sensors, airflow sensors, ventilation controllers,thermostats, occupancy measurements, and so on. In order for thesebuilding automation systems to properly analyze the amount ofinformation received from various points in the building, the systemmust have some knowledge of what type of information each pointprovides. Unfortunately, these points may not be classified or labeledconsistently, thereby hindering the building automation systems abilityto efficiently manage or control various equipment within the building.For example, some of the points may have been classified by amanufacturer according to one classification scheme, some of thesepoints may have been classified by an installer according to anotherclassification scheme, some of the points may have been classified bythe building owner, and so on. Furthermore, some of the points may nothave been classified at all. Accordingly, a thermostat may be labeled“THERMOSTAT,” “T,” “TSTAT,” “AC-00,” “DEVICE-1594-8916,” and so on.Without consistent classifications, the building automation systemcannot properly or efficiently monitor, manage, and report on variouselements within a building or group of buildings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an environment in which thebuilding automation facility may operate in accordance with someembodiments of the disclosed technology.

FIG. 2 is a block diagram illustrating the processing of a time-seriesclassification component.

FIG. 3 is a block diagram illustrating the processing of a meta dataclassification component.

FIG. 4 is a block diagram illustrating a number of sensors classifiedusing 1) Time-Series Classification and 2) Meta Classificationtechniques and 3) sensors that are left to classify.

FIG. 5 is a flow diagram illustrating the processing of a classifycomponent.

FIG. 6 is a block diagram illustrating some of the components that maybe incorporated in at least some of the computer systems and otherdevices on which the system operates and interacts with in someexamples.

DETAILED DESCRIPTION

A building automation space facility or building automation facilitycomprising systems and methods for classifying and standardizing sensorsand sensor data using time-series data and/or meta data, such as dataand meta information available in a control network or hardware (e.g.,internal memory or storage devices of sensors or other points) isdisclosed. In some cases, systems in buildings and portfolios ofbuildings have a large number of sensors. A skyscraper, for example,might have over 2 million unique data points coming from hundreds ofthousands of sensors, such as temperature sensors, Carbon Dioxidesensors, energy meters, water flow sensors, equipment status sensors(Boiler, Airhandler), humidity sensors, noise sensors, and so on. Thedisclosed system and methods provide techniques to rapidly identify andclassify sensors using analysis of time-series data (e.g., datacollected from a sensor such as by polling the sensor every second,minute, hour, day, month, and so on) and/or meta data (e.g., labelnames, group names, associated equipment, and so on) from these sensorsand associated control systems. In this manner, the disclosed techniquesaddress the problems presented to building automation facilities orsystems and greatly improve the extent to which a building automationfacility or system monitors, manages, and reports on various elementswithin a building or group of buildings.

A common problem in the setup and configuration of sensors used in, forexample, building automation systems (“BAS”) is the lack ofstandardization of control names between vendors as well as controlinstallers. The underlying controller hardware accepts a number ofsensor types, such as inputs and outputs, and allows users to define orname sensors with custom labels. These custom labels can make itdifficult for other users or systems to determine what the actual datais. For example, a label for Room Temperature in one system might be“RMTMP,” in another system it might be “Rm Temp,” and in another systemit might be “RT401.” On top of that, often times the sensors are notlabeled at all and they simply bear the name of the default port inwhich they are located. For example, a control system might have a portlabeled “AI 00,” a room temperature sensor connected to this port mightbe given the name “AI 00” in the control system, even though it is atemperature sensor.

The ability to rapidly identify and standardize the classification ofsensors is significant for making sure systems such, as buildingautomation controls, are operating properly, reports are accurate, aswell as extending an existing system to work with third partyapplications.

FIG. 1 is a block diagram illustrating an environment 100 in which abuilding automation facility may operate in accordance with someembodiments of the disclosed technology. In this embodiment, environment100 includes automation service provider 110, building automationfacility 120, building 130, points 135, controller 138 (e.g., heating,ventilation, air conditioning controller, building systems controller,etc.), and network 140. Automation service provider 110 includesbuilding automation facility 120, comprised of time-seriesclassification component 121, meta data classification component 122,classify component 123, classifier points data store 124, and accountsdata store 125. Time-series classification component 121 is invoked bythe building automation facility 120 to classify points based ontime-series data generated for each point and the classifier points.Meta data classification component 122 is invoked by the buildingautomation facility 120 to classify points based on meta data extractedfrom a point and classifier points. Classify component 123 is invoked bythe building automation facility 120 to classify points based ongraduated or hierarchical sets of previously-classified points.Classifier points data store 124 stores information for points that havealready been classified, such as statistical information, time-seriesdata, meta data, labels, classifications, manufacturer information,device attributes, device types, and so on. In some embodiments, theclassifier points data store serves as a classifier name library toprovide classifier names or classifications of classified points inresponse to queries. Accounts data store 125 stores information aboutone or more customers, such as an indication of points that areassociated with a customer, an indication of buildings that areassociated with a customer, and so on. Building 130 includes severalpoints 135 (interior and exterior) each configured to provide data toone or more building automation facilities. In this example, building130 includes its own building automation facility, which can be used toclassify points for the building 130 and other buildings (not shown). Insome embodiments, building automation facilities may share informationwith each other or other systems. The shared information may include,for example, statistical and trend information for points, accountinformation, and so on. In some embodiments, the various elements ofenvironment 100 communicate via network 140.

Classification of Sensors Based on Time-Series Interval Data

In some embodiments, the disclosed technology classifies sensors bycomparing their time-series data to a related sensor that has aclassification. By taking statistical factors of the time-series data,such as Minimum Value, Maximum Value, Average Value, Range, Mean. andother values, the sensors can be identified using clustering analysis.That is, taking time-series data from sensors that are labeled,comparing the time-series data to time series data of sensors that arenot labeled, and identifying sensors that have similar statisticalprofiles.

Benefits

Classification of sensors based on time-series data has significantsavings in the onboarding of new systems, the monitoring and maintenanceof sensors if one or more sensors goes offline or is reset, thecomparison of sensors and reporting across standards, and so on.

EXAMPLE

A controller could be connected to a number of sensors that represent azone (e.g., a room in a building). In that zone there could be onesensor that is a thermostat as well as other sensors that send data tocertain ports on the controller. Furthermore, the thermostat could havea setpoint sensor that has no label in the controller that identifies itas a thermostat setpoint sensor, such as “AI 00,” or a default pointlabel in a controller system. In the example below this is labeled“Unknown Sensor.”

Table 1 represents data taken from a number of labeled or classifiedsensors (i.e., “Room Pressure Setpoint,” “Room Temperature,” “RoomSupply Temperature,” and “Outdoor Air Cooling Lockout”) and oneunlabeled sensor (i.e., “Unknown Sensor”).

TABLE 1 Alias group max min mean median mode range Room Pressure AC_1I0.02 0.02 0.02 0.02 0.02 0 Setpoint Room AC_1I 68 72.5 72.8 72.85 72.60.9 Temperature Room Supply AC_1I 68.4 65.7 67.0 67 67 2.7 TemperatureOutdoor Air Cooling AC_1I 50 50 50 50 50 0 Lockout Unknown Sensor AC_1A66.6 65.4 65.9 65.8 65.7 1.2

In some embodiments, the disclosed technology uses a combination ofstatistical values to calculate a “distance” between the Unknown Sensorand the labeled sensors that have been classified. The larger thedistance, the less likely the Unknown Sensor is of the same type as aparticular labeled sensor. In this example, a distance can be calculatedas follows:

-   -   Difference Max Calculation (Diff Max)        -   Example: Unknown[Max value]−Classified Point[Max            value]=Difference Max    -   Difference Range Calculation (Diff Range)        -   Example: Unknown[Range]−Classified Point[Range]=Difference            Range    -   Distance Calculation        -   Example: (Difference Max)̂2+(Difference Range)̂2=Sum of            Squares.

TABLE 2 Diff Diff alias group max range Max Range Distance DescriptionRoom Pressure AC_1I 0.02 0 −66.58 −1.2 4434.33 Setpoint Room AC_1I 680.9 1.4 −.3 2.05 Closest Temperature Match Room Supply AC_1I 68.4 2.71.8 −1.5 5.49 Temperature Outdoor Air Cooling AC_1I 50 0 −16.6 −1.2 277Lockout Unknown Sensor AC_1A 66.6 1.2 0 0 0 Point to match

In Table 2, the labeled sensor producing the smallest distance (i.e.,2.05) from the “Unknown Sensor” is the sensor labeled “RoomTemperature.” Thus, the “Unknown Sensor” is most likely a RoomTemperature sensor. In some embodiments, the disclosed technology uses athreshold value to determine whether the closest match is “closeenough.” For example, the disclosed technology may establish a thresholdof 100 and ignore any sensor having a distance greater than 100 (thethreshold), even if it is the closest match (resulting in the UnknownSensor remaining unlabeled). In some cases, the system may prompt a userfor a label. Although this example uses Max Value and Range asstatistical values for calculating a distance, one of ordinary skill inthe art will recognize that any statistical value or combination ofstatistical values, such as max, min, median, mode, stdev, variance,rate of change, average rate of change, and others, can be used in thecalculation of a distance value. Furthermore, any number and combinationof values and their differences can be used to calculate a distance(e.g., sqrt(diffmodê2+diffstdev̂2+diffvariancê2+diffrateofchangê2)).

In addition, a weight can be added to each distance, for example averageand max value might be better at matching than min and median. So, aweight/multiplier can be added to the value to increase the matchlikelihood. Variables determined to be more important would have ahigher multiplier than variables determined to be less important.

Example Equation with Multiplier

(Difference Max)̂2*[Max Multiplier]+(Difference Average)̂2*[AverageMultiplier]

This multiplier can be hardcoded based on observation or can becalculated by comparing calculations based on a number of points. Forexample if the system knows that Outside Air Temperature and Indoor Airtemperature were often close to each other, the system could fine tunethe weight for each by calculating a percent match based on a knownOutside Air Temperature versus known Indoor Air Temperatures. Also, bychanging the sample time, such as comparing 30-minute increments versusday increments, can increase the likelihood of match. In addition,choosing specific days, such as weekdays versus weekends or holidaysversus non-holidays, can increase the accuracy of the match since manysensors are controlled or set by humans and these different groups ofdays can have different readings as a result.

Example Workflow for Time-Series Calculation

FIG. 2 is a block diagram illustrating the processing of a time-seriesclassification component in accordance with some embodiments of thedisclosed technology. In block 205, the component identifiespoints—e.g., data points (such as thermostats, occupancy counters, fanswitches, etc.) and sensors (such as temperature sensors, occupancysensors, humidity sensors, etc.)—to be classified and labeled. Forexample, the component may connect to a controller to communicate withvarious sensors and other data points to identify available pointsand/or points that have yet to be classified, named, and/or labeled. Inblocks 210-280, the component loops through each of the identifiedpoints to be classified and attempts to classify or label each point. Inblock 215, the component collects data from the currently-selected pointto be classified to develop trend data for the point. For example, thecomponent may poll the point periodically (e.g., every second, everyminute, every hour, every day) to collect a series of measurements fromthe point. This polling may take place over a predetermined period oftime, such as a minute, an hour, a day, a week, a month, and so on. Inblock 220, the component calculates statistics from the developed trenddata, such as a MEAN value, a MAX value, a MIN value, a MODE value, aMEDIAN value, a standard deviation, variance, and so on. In someembodiments, these statistics may be stored in association with eachpoint in the form of an array, vector, or other data structure. In block225, the components sets a MIN_DISTANCE value to a predeterminedMAX_VALUE. In block 230, the component identifies available classifierpoints (e.g., a training set of sensor labels or aliases of sensors thathave been classified). In blocks 235-260, the component loops througheach of the identified classifier points and compares statistics for acurrently-selected classifier point to statistics for thecurrently-selected point to be classified. In block 240, the componentcalculates the distance or difference between the currently-selectedclassifier point to statistics for the currently-selected point to beclassified based on the statistics for each. For example, the componentmay calculate the distance or difference between each of a plurality ofstatistical value pairs (e.g., the difference between a MEAN value forthe currently-selected classifier point and a MEAN value for thecurrently-selected point to be classified, the difference between a MINvalue for the currently-selected classifier point and a MIN value forthe currently-selected point to be classified, the difference between aMAX value for the currently-selected classifier point and a MAX valuefor the currently-selected point to be classified), square eachdifferences, and take the sum of the squares. One of ordinary skill inthe art will recognize that a distance or difference between two sets ofvalues (e.g., two sets of arrays or vectors) may be determined in anynumber of ways. In decision block 245, if the calculated distance ordifference is less than the current MIN_DISTANCE value, then thecomponent continues at block 250, else the component continues at block260. In block 250, the component sets the MIN_DISTANCE value equal tothe calculated distance or difference. In block 255, the component getsthe label for the currently-selected classifier point. In block 260, ifthere are any remaining classifier points the component loops back toblock 235 to select the next classifier point, else the componentcontinues at decision block 265. In decision block 265, if there is amatch between the currently-selected point to be classified and theclassifier points (i.e., if the MIN_DISTANCE value is below apredetermined threshold), then the component continues at block 270,else the component continues at block 275. In block 270, the componentsets the label for the currently-selected point to be classified to themost recently-retrieved label (block 255) by, for example, storing thelabel for the currently-selected point in the classifier point store,causing the point to store the label, and/or controlling thecurrently-selected point to write the label to internal storage, etc. Inblock 275, the component flags the currently-selected point to beclassified as not adequately matching with any of the classifier points.In block 280, if there are any remaining points to be classified (thathave not been flagged) the component loops back to block 210 to selectthe next point to be classified, else processing of the componentcompletes. In some embodiments, a means for performing a time-seriesclassification of one or more points comprises one or more computers orprocessors configured to carry out an algorithm disclosed in FIG. 2 andthis paragraph as described therein.

Classification of Sensors Based on Label and Meta Data

Often times the systems that connect the controllers together allow foruser input for naming data points and sensors. Installers andmanufactures often have different naming conventions. Outside AirTemperature might be labeled as “Outside Air” in one system, “OAT” inanother, and “Outside Air Temperature” in yet another. Furthermore,since many of the systems require someone to manually enter the names,the labels can be mistyped, such as “Ou Air Temper,” “OutATemp,” and soon.

The classification of sensor data using labels and meta data bycomparing names and meta data from a training set of sensor labels oraliases of sensors that have been classified is described below.

Classification Example

For example, a sensor that measures the temperature outdoors can belabeled in a number of ways. Example labels for a sensor that measuresOutdoor Air Temperature include:

-   -   OAT    -   Out Door Air Temperature    -   Out Door Ar Temp    -   OATemp

All of these sensor names could be used to classify a correspondingsensor as an Outdoor Air Temperature sensor. In the example below, thename “Alias” is used for the classification label for sensors that havebeen classified.

In some embodiments, the disclosed technology classifies these labels byseparating them into constituent parts according to one or moredelimiter rules, such as CamelCase, spacing, or other specialcharacters/delimiters. Since users often use abbreviated versions ofwords, such as “OAT” for Outside Air Temperature, as well as misspellwords (e.g., “Ar” instead of “Air”), the disclosed technology can do aninitial match using just the first letter of the CamelCase letter foreach tag. The results of separating the above-mentioned labels accordingto CamelCase rules are provided below in Table 3A. In this example, eachseparate portion is labeled as a separate “Tag.”

TABLE 3A Sensor Name Tag1 Tag2 Tag3 Tag4 OAT O A T Out Door AirTemperature Out Door Air Temperature Out Door Ar Temp Out Door Ar TempOATemp O A Temp

TABLE 3B Classifier Name Tag1 Tag2 Tag3 Tag4 Outdoor Air TemperatureOutdoor Air Temperature

The results of separating the above-mentioned labels according toCamelCase rules and using only the first letter of each tag are providedbelow in Table 4A. In this example, each separate portion is labeled asa separate “Tag.” Comparing the tags of each sensor name in Table 3A orTable 4A to the tags of a classifier name (i.e., the label of a sensorthat has been classified), such as “Outdoor Air Temperature” in Table 3Bor Table 4B, the disclosed technology can generate a match percentage.The match percentage is the total number of tags that match between theclassifier name and the sensor name divided by the total number of tagsassociated with the sensor name.

TABLE 4A Sensor Name Tag1 Tag2 Tag3 Tag4 Match Percent Alias OAT O A T3/3 = 100% Not identified Out Door Air Temperature O D A T 3/4 = 75% Not identified Out Door Ar Temp O D A T 3/4 = 75%  Not identified OATempO A T 3/3 = 100% Not identified

TABLE 4B Classifier Name Tag1 Tag2 Tag3 Tag4 Match Percent Alias OutdoorAir Temperature O A T Classifier Outside Air Temperature

Example calculations: For the “Out Door Air Temperature” sensor name inTable 4A, there are four total tags: ‘O,’ ‘D,’ ‘A’ and ‘T’ and three ofthese tags match a tag of the classifier “Outdoor Air Temperature”(i.e., ‘O,’ ‘A,’ and ‘T’). Accordingly, the match percentage is 75%(i.e., ¾). For the ‘OAT’ sensor name in table 4, there are three totaltags: ‘O,’ ‘A,’ and ‘T’ and all three of these tags match. Accordingly,the match percentage is 100% (i.e., 3/3). In some embodiments, thedisclosed technology uses a library of classifier names, comparing oneor more names in the library to sensor names for non-classified sensorsand, for each non-classified sensor, attributes the classifier name withthe highest match percentage to the non-classified sensor that exceeds apredetermined threshold (e.g., 0%, 10%, 50%, 75%, 95%, etc.).

Example Classifying Against Sensors Grouped in a Controller

While the above example works for classifying sensors in general, theaccuracy of identification can be increased by comparing sensors thatare linked to the same controller. A controller could be connected or incommunication with sensors that have labels such as, “Return Air Temp,”“Outdoor Air Temperature,” and “Set Point” in addition to an unlabeledsensor. Using the match percentage technique disclosed above and using amatch percentage threshold (e.g., 70%) the sensor that measures theoutdoor air temperature can be classified.

The example table below shows a sensor with the label “OutDoor AirTemperature” being classified.

In this example, the label “Return Air Temp” has three tags: ‘R,’ ‘A,’and ‘T,’ and the classifier name “Outdoor Air Temperature” has threetags: ‘O,’ ‘A,’ and ‘T.’ Two of the sensor's three tags match (i.e., ‘A’and ‘T’), resulting in a match percentage of 66.6%.

TABLE 5A Sensor Name Tag1 Tag2 Tag3 Tag4 Match Return Air Temp R A T  ⅔= 66.6% OutDoor Air Temperature O D A T ¾ = 75% Set Point S P 0/3 = 0% AI 00 A I 0 0 ¼ = 25%

TABLE 5B Classifier Name Tag1 Tag2 Tag3 Tag4 Match Outdoor AirTemperature O A T Classifier

Simplifying Classification by Grouping Sensors Based on System Type

In some embodiments, the sensors being classified are grouped by thetype of system being classified. For example a controller thatrepresents a room or zone in the real world would have a number ofsensors or other points, such as a Room Temperature sensor, Set Point,and occupancy sensor. An Airhandler on the other hand would have anOutdoor Air Temperature sensor, Return Air Temperature sensor, and RoomTemperature sensors. A determination of what type of sensors belong towhat type of system can be done before the classification by, forexample, taking a point or sensor's meta data (e.g., what controller thesensor is under or information in the name, such as points that have theequipment or room name in them). An example point might have the name“Rm203 Rm Temp” which implies it can be grouped with other points thathave “Rm203” in their name. Similarly, if a group of points (sensors)are each associated with a particular controller model (e.g., VAV-SD2A),these points and be grouped together. By identifying what sensors in acontroller belong to specific physical pieces of equipment, and limitingthe classifiers to that type of equipment, the disclosed technology canclassify individual sensors and groups of sensors with more accuracy andefficiency.

EXAMPLE

A building might have sensors such as those represented in Table 6A.Those sensors belong to certain controllers that represent an AirHandler(AHU), a zone, and general sensors (Global). Matching based on tagging,as described, is applied to a number of sensors in a building, and thosesensors are associated with groups that correspond with the type ofequipment they are part of.

The tags “R,” “A,” and “T” are created for the “Return Air Temperature”name using the system described above. Those tags are then compared to aclassifier name or library of classifier names that are specific to agroup type (e.g., a library of classifier names specific to a “Room”group comprising the following classifier names: “Room Temperature,”“Room Humidity,” and “Room Cooling Setpoint”). The “Room Temperature”name has the tags “R” and “T.” The match percentage between “RoomTemperature” and “Return Air Temperature” is calculated by dividing thetotal number of tags in the point or sensor name (or label) that matchthe tags in the classifier name and dividing by the total number of tagsin the point or sensor name.

2 Match/3 total=66.6%.

The results are shown in the table below. Although the “RM Temp” nameand the “Return Air Temp” name both have the same match percentage(i.e., 66.6%), the “Return Air Temp” name can be eliminated because thegroup it belongs to is not a zone, whereas the classifier name belongsto the zone group.

TABLE 6A Sensor Name Group Tag1 Tag2 Tag3 Tag4 Match Outcome Return AirTemperature AHU R A T 2/3 = 66.6% Eliminated, not a zone group OutDoorAir Temperature Global O D A T 2/4 = 50%  Set Point Zone S P 0/3 = 0%  AI 00 Zone A I 0 0 0/4 = 0%   RM Temp Zone R M T 2/3 = 66.6% Highestmatch in zone

TABLE 6B Classifier Name Group Tag1 Tag2 Tag3 Tag4 Match Outcome RoomTemperature Zone R T Classifier

Example Workflow for Metadata Classification

FIG. 3 is a block diagram illustrating the processing of a meta dataclassification component in accordance with some embodiments of thedisclosed technology. In block 310, the component identifies points tobe classified. In block 315, the component collects meta data from eachpoint, such as manufacturer's information, any current labels for thepoint, a location for the point, a type for the point, any current tagsfor the point, and so on. In block 320, the component groups points tobe classified. For example, the component may group points based onmetadata such as which rooms each point is in, which controller isassociated with or responsible for the point, or other groupingtechniques described above. In blocks 325-365, the component loopsthrough each point to be classified and attempts to classify the point.In block 330, the component selects a set of classification rules, suchas splitting any labels associated with the point into constituent parts(e.g., words, letters, phonemes) and identifying matches to classifierpoints, splitting any labels associated with the point into constituentfirst letters and identifying matches to classifier points, comparingsets of non-classified points in one zone or area to sets of classifiedpoints in another zone or area, and so on. In blocks 335-345, thecomponent loops through each of the points in the group of points to beclassified and applies the selected classification rules to each point.In block 340, the component applies the selected classification rules tothe currently-selected point in the group and, if there is a sufficientmatch (e.g., the match percentage exceeds a predetermined threshold),then the point in the group is assigned a label based on the matchingclassifier point. In block 345, if there are any remaining points in thegroup of points to be classified, the component loops back to block 335to select the next point in the group, else the component continues atdecision block 350. In decision block 350, if all of the points in thegroup have been classified, then the component continues at block 365,else the component continues at decision block 355. In decision block355, if there are additional classification rules, then the componentloops back to block 330, else the component continues at block 360. Inblock 360, the component flags any points that were not classified. Inblock 365, if there are any remaining groups of points to be classified,the component loops back to block 325 to select the next group, elseprocessing of the component completes. In some embodiments, a means forperforming a meta data classification of one or more points comprisesone or more computers or processors configured to carry out an algorithmdisclosed in FIG. 3 and this paragraph as described therein.

Classification of Sensors Based on Meta Data with Time-Series IntervalData

A more advanced filter can be created by systematically going throughthe sensors that are labeled in the system and applying combinations ofthe time-series classification and meta data/label classification.

EXAMPLE

FIG. 4 is a block diagram illustrating a number of sensors classifiedusing 1) Time-Series Classification and 2) Meta Classificationtechniques and 3) sensors that are not yet classified. In this example,a Heating Status sensor is identified using a time-seriesclassification. After that, a meta classification technique is run onthe remaining sensors and the Room Setpoint and Heating Status sensorsare identified. Lastly the sensor labeled “AC-20,” which was notclassified using the time-series or meta classification techniques, isflagged for a user to look at because, for example, the sensor could beset up or grouped incorrectly. This example illustrates theclassification of points in one room (Room 234) 410 by comparing thepoints therein to classified points in another room (Room 121) 415. In abuilding, unlabeled points (e.g., sensors) in one room could beclassified and labeled based on sensors in a similar room. Thus, if thesensors in Room 234 were classified and the sensors in Room 121 were notclassified, the system could use a number of methods disclosed herein toclassify sensors in Room 121 based on information gathered from thesensors in Room 234. For example, meta classification could be appliedto identify two sensors as “Room Temperature” and “SetPoint” based oncurrent labels of points 430 (“Room Temperature”) and 445 (“RMTemp”) and440 (“Room Setpoint”) and 435 (“SP”), respectively. This leaves AC-00425 and AC-20 455 as remaining sensors that need to be classified.Applying a time-series data analysis, may identify AC-00 425 in thenon-classified room (Room 121) as a Heating Status point based onstatistical values generated from trend data for points 420 and 425. Inthis example, Room Highlimit point AC-02 450 was not used to classifyany of the non-classified points shown, although. After the algorithmsare run, any remaining unclassified sensors can be flagged asunclassified. In this manner, any combination of time-seriesclassification and meta data classification may be employed to classifypoints in various buildings and rooms.

FIG. 5 is a flow diagram illustrating the processing of a classifycomponent in accordance with some embodiments of the disclosedtechnology. The classify component is invoked to classify points basedon graduated sets of previously-classified points (i.e., classifierpoints). In block 510, the component identifies points to be classified.In block 520, the component identifies classified local points, such asclassified points that are in the same room or building of the points tobe classified, points that are controlled by the same controller, and soon. In some embodiments, this information may be retrieved from thepoints themselves, from a controller in communication with the points,and so on. In block 530, the component invokes one or moreclassification components (e.g., a time-series classification component,a meta data classification, or both) to attempt to classify theidentified points using the classified local points as the classifierpoints. In decision block 535, if all of the points to be classified areclassified then processing of the component completes, else thecomponent continues at block 540. In block 540, the component identifiesclassified account points, such as classified points that are in abuilding associated with the same account (i.e., customer) as the pointsto be classified. In some embodiments, this information may be retrievedfrom the points themselves, from a controller in communication with thepoints, and so on. In block 550, the component invokes one or moreclassification components (e.g., a time-series classification component,a meta data classification, or both) to attempt to classify theidentified points using the classified account points as the classifierpoints. In decision block 555, if all of the points to be classified areclassified then processing of the component completes, else thecomponent continues at block 560. In block 560, the component identifiesall other classified points (i.e., non-local, non-account points) from,for example, a classifier data store or classifier name data store. Inblock 570, the component invokes one or more classification components(e.g., a time-series classification component, a meta dataclassification, or both) to attempt to classify the identified pointsusing the other classified points as classifier points. In block 580 thecomponent flags any non-classified points and then completes. In someembodiments, a means for performing a hierarchical classification of oneor more points comprises one or more computers or processors configuredto carry out an algorithm disclosed in FIG. 5 and this paragraph in theorder described therein.

FIG. 6 below is a block diagram illustrating some of the components thatmay be incorporated in at least some of the computer systems and otherdevices on which the system operates and interacts with in someexamples. In various examples, these computer systems and other devices600 can include server computer systems, desktop computer systems,laptop computer systems, netbooks, tablets, mobile phones, personaldigital assistants, televisions, cameras, automobile computers,electronic media players, and/or the like. In various examples, thecomputer systems and devices include one or more of each of thefollowing: a central processing unit (“CPU”) 601 configured to executecomputer programs; a computer memory 602 configured to store programsand data while they are being used, including a multithreaded programbeing tested, a debugger, an operating system including a kernel, anddevice drivers; a persistent storage device 603, such as a hard drive orflash drive configured to persistently store programs and data; acomputer-readable storage media drive 604, such as a floppy, flash,CD-ROM, or DVD drive, configured to read programs and data stored on acomputer-readable storage device, such as a floppy disk, flash memorydevice, a CD-ROM, a DVD; and a network connection 605 configured toconnect the computer system to other computer systems to send and/orreceive data, such as via the Internet, a local area network, a widearea network, a point-to-point dial-up connection, a cell phone network,or another network and its networking hardware in various examplesincluding routers, switches, and various types of transmitters,receivers, or computer-readable transmission media. While computersystems configured as described above may be used to support theoperation of the disclosed techniques, those skilled in the art willreadily appreciate that the disclosed techniques may be implementedusing devices of various types and configurations, and having variouscomponents. Elements of the disclosed systems and methods may bedescribed in the general context of computer-executable instructions,such as program modules, executed by one or more computers or otherdevices. Generally, program modules include routines, programs, objects,components, data structures, and/or the like configured to performparticular tasks or implement particular abstract data types and may beencrypted. Moreover, the functionality of the program modules may becombined or distributed as desired in various examples. Moreover,display pages may be implemented in any of various ways, such as in C++or as web pages in XML (Extensible Markup Language), HTML (HyperTextMarkup Language), JavaScript, AJAX (Asynchronous JavaScript and XML)techniques or any other scripts or methods of creating displayable data,such as the Wireless Access Protocol (“WAP”).

The following discussion provides a brief, general description of asuitable computing environment in which the invention can beimplemented. Although not required, aspects of the invention aredescribed in the general context of computer-executable instructions,such as routines executed by a general-purpose data processing device,e.g., a server computer, wireless device or personal computer. Thoseskilled in the relevant art will appreciate that aspects of theinvention can be practiced with other communications, data processing,or computer system configurations, including: Internet appliances,hand-held devices (including personal digital assistants (PDAs)),heating, ventilation, and air-conditioning controller (HVAC) controller,special-purpose HVAC controller, programmable HVAC controller, wearablecomputers, all manner of cellular or mobile phones (including Voice overIP (VoIP) phones), dumb terminals, media players, gaming devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, set-top boxes, network PCs, mini-computers, mainframecomputers, and the like. Indeed, the terms “computer,” “server,” “host,”“host system,” and the like are generally used interchangeably herein,and refer to any of the above devices and systems, as well as any dataprocessor.

Aspects of the invention can be embodied in a special purpose computeror data processor that is specifically programmed, configured, orconstructed to perform one or more of the computer-executableinstructions explained in detail herein. While aspects of the invention,such as certain functions, are described as being performed exclusivelyon a single device, the invention can also be practiced in distributedenvironments where functions or modules are shared among disparateprocessing devices, which are linked through a communications network,such as a Local Area Network (LAN), Wide Area Network (WAN), or theInternet. In a distributed computing environment, program modules may belocated in both local and remote memory storage devices.

Aspects of the invention may be stored or distributed oncomputer-readable storage media, including magnetically or opticallyreadable computer discs, hard-wired or preprogrammed chips (e.g., EEPROMsemiconductor chips), nanotechnology memory, biological memory, or otherdata storage media but not including transitory, propagating signals.Alternatively, computer implemented instructions, data structures,screen displays, and other data under aspects of the invention may bedistributed over the Internet or over other networks (including wirelessnetworks), on a propagated signal on a computer-readable propagationmedium or a computer-readable transmission medium (e.g., anelectromagnetic wave(s), a sound wave, etc.) over a period of time, orthey may be provided on any analog or digital network (packet switched,circuit switched, or other scheme). Non-transitory computer-readablemedia include tangible media such as hard drives, CD-ROMs, DVD-ROMS, andmemories such as ROM, RAM, and Compact Flash memories that can storeinstructions and other storage media. Signals on a carrier wave such asan optical or electrical carrier wave are examples of transitorycomputer-readable media. “Computer-readable storage media,” as usedherein, comprises all computer-readable media except for transitory,propagating signals.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above Detailed Description of examples of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above. While specific examples for the invention are describedabove for illustrative purposes, various equivalent modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize. For example, while processes or blocks arepresented in a given order, alternative implementations may performroutines having steps, or employ systems having blocks, in a differentorder, and some processes or blocks may be deleted, moved, added,subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes or blocks may be implemented ina variety of different ways. Also, while processes or blocks are attimes shown as being performed in series, these processes or blocks mayinstead be performed or implemented in parallel, or may be performed atdifferent times. Further any specific numbers noted herein are onlyexamples: alternative implementations may employ differing values orranges.

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the invention. Some alternativeimplementations of the invention may include not only additionalelements to those implementations noted above, but also may includefewer elements.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. For example, while several ofthe examples provided above are described in the context of sensors, oneof ordinary skill in the art will recognize that these techniques can beapplied to other points, such as controllers, data sources, and so on.Details of the system may vary considerably in its specificimplementation, while still being encompassed by the invention disclosedherein. As noted above, particular terminology used when describingcertain features or aspects of the invention should not be taken toimply that the terminology is being redefined herein to be restricted toany specific characteristics, features, or aspects of the invention withwhich that terminology is associated. In general, the terms used in thefollowing claims should not be construed to limit the invention to thespecific examples disclosed in the specification, unless the aboveDetailed Description section explicitly defines such terms. Accordingly,the actual scope of the invention encompasses not only the disclosedexamples, but also all equivalent ways of practicing or implementing theinvention under the claims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesthe various aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C. § 112(f), other aspects maylikewise be embodied as a means-plus-function claim, or in other forms,such as being embodied in a computer-readable medium. (Any claimsintended to be treated under 35 U.S.C. § 112(f) will begin with thewords “means for”, but use of the term “for” in any other context is notintended to invoke treatment under 35 U.S.C. § 112(f).) Accordingly, theapplicant reserves the right to pursue additional claims after filingthis application to pursue such additional claim forms, in either thisapplication or in a continuing application. To the extent any materialsincorporated herein by reference conflict with the present disclosure,the present disclosure controls.

We claim:
 1. A computer-readable storage medium storing instructionsthat, if executed by a building automation space system having aprocessor, cause the building automation space system to perform amethod for classifying points in one or more buildings, the methodcomprising: for each of a first plurality of non-classified points ofthe one or more buildings, retrieving a point name for thenon-classified point of the second plurality of non-classified points,for each of a plurality of classifier names, determining a matchpercentage for the non-classified point of the first plurality ofnon-classified points at least in part by comparing the classifier nameto the point name retrieved for the non-classified point of the firstplurality of non-classified points, determining whether the largestmatch percentage determined for the non-classified point of the firstplurality of non-classified points is greater than a first predeterminedthreshold, and in response to determining that the largest matchpercentage determined for the non-classified point of the firstplurality of non-classified points is greater than the firstpredetermined threshold, identifying a classifier name with the largestdetermined match percentage for the point name retrieved for thenon-classified point.
 2. The computer-readable storage medium of claim1, wherein at least one of the points is a sensor and wherein at leastone of the points is not a sensor.
 3. The computer-readable storagemedium of claim 1, wherein the points include water flow sensors,equipment status sensors, humidity sensors, and noise sensors.
 4. Thecomputer-readable storage medium of claim 1, wherein the method furthercomprises: in response to determining that the largest match percentagedetermined for the non-classified point of the first plurality ofnon-classified points is greater than the second predeterminedthreshold, attributing, to the non-classified point of the firstplurality of non-classified points, the identified classifier name withthe largest determined match percentage for the point name retrieved forthe non-classified point.
 5. The computer-readable storage medium ofclaim 1, wherein the method further comprises: for each of a thirdplurality of non-classified sensors, wherein the third plurality ofnon-classified sensors is a subset of the second plurality ofnon-classified sensors, flagging the non-classified sensor of the thirdplurality of non-classified sensors for no match.
 6. Thecomputer-readable storage medium of claim 5, wherein each of theplurality of classified sensors is selected from a group of classifiedlocal sensors, wherein the method further comprises: for each of theflagged non-classified sensors of the third plurality of non-classifiedsensors, attempting to classify the non-classified sensor based onclassified sensors selected from a group of classified account sensors.7. A method, performed by a computing system having a processor and amemory, for classifying points in one or more buildings, the methodcomprising: for each of a first plurality of non-classified points ofthe one or more buildings, retrieving a point name for thenon-classified point of the second plurality of non-classified points,for each of a plurality of classifier names, determining a matchpercentage for the non-classified point of the first plurality ofnon-classified points at least in part by comparing the classifier nameto the point name retrieved for the non-classified point of the firstplurality of non-classified points, determining whether the largestmatch percentage determined for the non-classified point of the firstplurality of non-classified points is greater than a first predeterminedthreshold, and in response to determining that the largest matchpercentage determined for the non-classified point of the firstplurality of non-classified points is greater than the firstpredetermined threshold, identifying a classifier name with the largestdetermined match percentage for the point name retrieved for thenon-classified point.
 8. The method of claim 7, wherein the pointsinclude temperature sensors, Carbon Dioxide sensors, and energy meters9. The method of claim 7, wherein the points include water flow sensors,equipment status sensors, humidity sensors, and noise sensors
 10. Themethod of claim 7, wherein determining a match percentage for a firstnon-classified sensor of the second plurality of non-classified sensorsand a first name of the classifier name library comprises: separatingthe retrieved name for the non-classified sensor of the second pluralityof non-classified sensors according to a delimiter rule; separating thefirst name of the classifier name library into constituent partsaccording to a delimiter rule; and comparing the constituent parts ofthe separated name for the non-classified sensor of the second pluralityof non-classified sensors to constituent parts of the first name of theclassifier name library.
 11. The method of claim 10, wherein comparingthe constituent parts of the separated name for the first non-classifiedsensor of the second plurality of non-classified sensors to constituentparts of the separated first name of the classifier name librarycomprises: for each constitute part of the retrieved name for the firstnon-classified sensor of the second plurality of non-classified sensors,comparing the constituent part to the constituent parts of the firstname of the classifier name library to determine whether the constituentpart of the retrieved name for the first non-classified sensor of thesecond plurality of non-classified sensors matches at least one of theconstituent parts of the separated first name of the classifier namelibrary; and wherein determining a match percentage for the firstnon-classified sensor comprises: determining a number of constituentparts of the retrieved name for the first non-classified sensor of thesecond plurality of non-classified sensors that match at least one ofthe constituent parts of the separated first name of the classifier namelibrary, and a total number of constituent parts of the retrieved namefor the first non-classified sensor of the second plurality ofnon-classified sensors.
 12. The method of claim 10, wherein eachconstituent part is a letter.
 13. The method of claim 10, wherein atleast one of the constituent parts is a word.
 14. The method of claim10, wherein at least one of the constituent parts is a phoneme.
 15. Abuilding automation system configured to classify points in one or morebuildings, the building automation system comprising: at least oneprocessor; at least one memory; a component configured to, for each of afirst plurality of non-classified points of the one or more buildings,retrieve a point name for the non-classified point of the secondplurality of non-classified points, for each of a plurality ofclassifier names, determine a match percentage for the non-classifiedpoint of the first plurality of non-classified points at least in partby comparing the classifier name to the point name retrieved for thenon-classified point of the first plurality of non-classified points,determine whether the largest match percentage determined for thenon-classified point of the first plurality of non-classified points isgreater than a first predetermined threshold, and in response todetermining that the largest match percentage determined for thenon-classified point of the first plurality of non-classified points isgreater than the first predetermined threshold, identify a classifiername with the largest determined match percentage for the point nameretrieved for the non-classified point, wherein the component comprisescomputer-readable instructions stored in the at least one memory forexecution by the at least one processor.
 16. The building automationsystem of claim 15, wherein the points include water flow sensors,equipment status sensors, humidity sensors, and noise sensors.
 17. Thebuilding automation system of claim 15, wherein each of the firstplurality of non-classified sensors are in one room of a first buildingand wherein each of the plurality of classified sensors is selected froma group of sensors in a different room of the first building.
 18. Thebuilding automation system of claim 15, further comprising: a componentconfigured to, for each of a plurality of statistical measures,determine a value of the statistical measure for a first classifiedsensor, determine a value of the statistical measure for a firstnon-classified sensor, and determine a difference between the determinedvalues of the statistical measure.
 19. The building automation system ofclaim 18, further comprising: a component configured to, for each of thedetermined differences, determine the square of the determineddifference; a component configured to determine the sum of thedetermined squares; and a component configured to determine the squareroot of the determined sum.
 20. The method of claim 1, wherein thesecond plurality of non-classified sensors is a subset of the firstplurality of non-classified sensors.